





'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_   :       Union[str, Any]       =     logging.get_logger(__name__)

lowerCAmelCase_   :       Any       =     {'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_   :       str       =     {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

lowerCAmelCase_   :       int       =     {
    'camembert-base': 5_12,
}

lowerCAmelCase_   :       Tuple       =     '▁'
class 							__SCREAMING_SNAKE_CASE    (_UpperCAmelCase ):






				"""simple docstring"""







				__a  =VOCAB_FILES_NAMES
				__a  =PRETRAINED_VOCAB_FILES_MAP
				__a  =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a  =['input_ids', 'attention_mask']




				def __init__(    self	:   List[Any]   ,							__a	:   Union[str, Any]   ,							__a	:   Any="<s>"   ,							__a	:   List[Any]="</s>"   ,							__a	:   str="</s>"   ,							__a	:   Any="<s>"   ,							__a	:   Any="<unk>"   ,							__a	:   Tuple="<pad>"   ,							__a	:   List[Any]="<mask>"   ,							__a	:   Optional[Any]=["<s>NOTUSED", "</s>NOTUSED"]   ,							__a	:   Tuple = None   ,							**__a	:   Optional[int]   ,							):
								# Mask token behave like a normal word, i.e. include the space before it
								_a				      =					AddedToken(__a   ,							lstrip=__a   ,							rstrip=__a   ) if isinstance(__a   ,							__a   ) else mask_token

								_a				      =					{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__a   ,							eos_token=__a   ,							unk_token=__a   ,							sep_token=__a   ,							cls_token=__a   ,							pad_token=__a   ,							mask_token=__a   ,							additional_special_tokens=__a   ,							sp_model_kwargs=self.sp_model_kwargs   ,							**__a   ,							)
								_a				      =					spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.Load(str(__a   )   )
								_a				      =					vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								_a				      =					{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
								_a				      =					len(self.fairseq_tokens_to_ids   )
								_a				      =					len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
								_a				      =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}




				def     UpperCamelCase__  (    self	:   Any   ,							__a	:   List[Any]   ,							__a	:   List[Any] = None   ):

								if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								_a				      =					[self.cls_token_id]
								_a				      =					[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep




				def     UpperCamelCase__  (    self	:   List[Any]   ,							__a	:   str   ,							__a	:   int = None   ,							__a	:   Tuple = False   ):
								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__a   ,							token_ids_a=__a   ,							already_has_special_tokens=__a   )

								if token_ids_a is None:
												return [1] + ([0] * len(__a   )) + [1]
								return [1] + ([0] * len(__a   )) + [1, 1] + ([0] * len(__a   )) + [1]




				def     UpperCamelCase__  (    self	:   Dict   ,							__a	:   Dict   ,							__a	:   int = None   ):
								_a				      =					[self.sep_token_id]
								_a				      =					[self.cls_token_id]

								if token_ids_a is None:
												return len(cls + token_ids_a + sep   ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]




				@property
				def     UpperCamelCase__  (    self	:   Optional[Any]   ):
								return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )




				def     UpperCamelCase__  (    self	:   Dict   ):
								_a				      =					{self.convert_ids_to_tokens(__a   ): i for i in range(self.vocab_size   )}
								vocab.update(self.added_tokens_encoder   )
								return vocab




				def     UpperCamelCase__  (    self	:   List[Any]   ,							__a	:   Optional[Any]   ):
								return self.sp_model.encode(__a   ,							out_type=__a   )




				def     UpperCamelCase__  (    self	:   List[str]   ,							__a	:   Tuple   ):
								if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(__a   ) == 0:
												# Convert sentence piece unk token to fairseq unk token index
												return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(__a   )




				def     UpperCamelCase__  (    self	:   Union[str, Any]   ,							__a	:   List[Any]   ):
								if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset   )




				def     UpperCamelCase__  (    self	:   List[Any]   ,							__a	:   Optional[Any]   ):
								_a				      =					[]
								_a				      =					""
								_a				      =					False
								for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																if not prev_is_special:
																				out_string += " "
																out_string += self.sp_model.decode(__a   ) + token
																_a				      =					True
																_a				      =					[]
												else:
																current_sub_tokens.append(__a   )
																_a				      =					False
								out_string += self.sp_model.decode(__a   )
								return out_string.strip()




				def __getstate__(    self	:   Dict   ):
								_a				      =					self.__dict__.copy()
								_a				      =					None
								return state




				def __setstate__(    self	:   Union[str, Any]   ,							__a	:   int   ):
								_a				      =					d

								# for backward compatibility
								if not hasattr(self   ,							"sp_model_kwargs"   ):
												_a				      =					{}

								_a				      =					spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.Load(self.vocab_file   )




				def     UpperCamelCase__  (    self	:   str   ,							__a	:   Tuple   ,							__a	:   List[str] = None   ):
								if not os.path.isdir(__a   ):
												logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
												return
								_a				      =					os.path.join(
								    __a   ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(__a   ) and os.path.isfile(self.vocab_file   ):
												copyfile(self.vocab_file   ,							__a   )
								elif not os.path.isfile(self.vocab_file   ):
												with open(__a   ,							"wb"   ) as fi:
																_a				      =					self.sp_model.serialized_model_proto()
																fi.write(__a   )

								return (out_vocab_file,)







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  ["RoFormerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RoFormerForCausalLM",
           "RoFormerForMaskedLM",
           "RoFormerForMultipleChoice",
           "RoFormerForQuestionAnswering",
           "RoFormerForSequenceClassification",
           "RoFormerForTokenClassification",
           "RoFormerLayer",
           "RoFormerModel",
           "RoFormerPreTrainedModel",
           "load_tf_weights_in_roformer",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRoFormerForCausalLM",
           "TFRoFormerForMaskedLM",
           "TFRoFormerForMultipleChoice",
           "TFRoFormerForQuestionAnswering",
           "TFRoFormerForSequenceClassification",
           "TFRoFormerForTokenClassification",
           "TFRoFormerLayer",
           "TFRoFormerModel",
           "TFRoFormerPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FlaxRoFormerForMaskedLM",
           "FlaxRoFormerForMultipleChoice",
           "FlaxRoFormerForQuestionAnswering",
           "FlaxRoFormerForSequenceClassification",
           "FlaxRoFormerForTokenClassification",
           "FlaxRoFormerModel",
           "FlaxRoFormerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






